AI in Australian contact centres - where are we really?

It feels like every vendor at every conference right now is pitching the exact same AI story, promising massive transformations and the 'contact centre of the future.'

But let's look at what is really going on. The 2026 Contact Centre Best Practice Report surveyed over 600 people across the Australian industry. Kaizn presents a chapter at the SMAART Best Practice Roadshow each year. This is our fifth year presenting, and we presented findings on AI for self-service. 

Here’s a quick summary of some points in the AI section that stuck with me.

Most contact centres are watching

44% of contact centres describe their current AI position as "only analysing calls and data."

That's the most common response by a significant margin. Only 4% are scaling end-to-end automation across multiple workflows. Full-scale AI deployment remains the exception.

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The barriers are people and process problems

Budget limitations top the list at 54%. The internal capability gaps are just as telling.

What's limiting automation:

• Technical implementation skills — 55%
• Process design — 44%
• Governance and compliance expertise — 43%
• Change management — 42%

More AI software doesn't fix a process design gap or a change management problem. This is where most organisations stall after the initial deployment.

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Data readiness is the problem nobody's confronting

3% of contact centres are completely confident in their data foundations.

AI automation, AI agents, AI self-service — all of it runs on data. Unreliable data produces unreliable outcomes, and unreliable outcomes kill internal confidence to scale.

Adrienne Merlo from Customer Driven put it plainly in the report: "You can't automate a broken process or fuel a genius tool with garbage data."

Organisations treating data readiness as a parallel workstream will find their AI ambitions harder to achieve and more expensive.

Where AI is delivering results

Post-call automation has the most traction:

• Automatic call summarisation — 35% using it, strong momentum
• Real-time agent assist — the during-call technology with the highest combined adoption and experimentation rate
• Speech and interaction analytics — 22% using, 29% experimenting

These are the use cases producing early wins. High-visibility, lower-complexity, and the results show up immediately for frontline agents.

For AI self-service specifically:

• 53% of contact centres are using some form of self-service AI
• Two thirds have built it to handle basic information retrieval
• 13% achieve a completely seamless AI-to-human handover
• 77% ask customers to repeat information when escalating to a human agent

The foundations are there. The scope of what AI self-service is being asked to do remains limited.

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Voice AI: strategy over curiosity

71% of contact centres expect their use of Voice AI to increase over the next two years.

Right now, 80% have no Voice AI deployed. Of those who use it, 60% describe it as somewhat or highly effective, while 40% are undecided or too early to tell.

Voice AI is moving from a point of curiosity to a point of strategy. The organisations building capability now will be better placed when deployment becomes standard

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The window is narrowing

The report is direct: the groundwork laid in 2026 will determine who is best placed to scale in the years ahead. For contact centres still in observation mode, catching up without incurring significant costs becomes harder as automation becomes standard.

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Daniel Harding is Founder and Director of Kaizn, an independent CX and AI advisory helping contact centres across ANZ make better technology decisions.

Want to talk through where your organisation sits with AI automation, AI agents, or AI self-service? Book a conversation here.